Let’s say you’re leading a meeting about the hourly pay of your company’s warehouse employees. For several years it has automatically been increased by small amounts to keep up with inflation. Citing a study of a large company that found that higher pay improved productivity so much that it boosted profits, someone on your team advocates for a different approach: a substantial raise of $2 an hour for all workers in the warehouse. What would you do?

Too often business leaders go in one of two directions in these moments: either taking the evidence presented as gospel or dismissing it altogether. Both approaches are misguided. Leaders instead should organize discussions that thoughtfully evaluate seemingly relevant evidence and its applicability to a given situation.

In the scenario just described you should pose a series of questions aimed at assessing the potential impact of wage increases on your company specifically. You might ask:

• Can you tell us more about the setting of the research to help us evaluate whether it applies to our warehouse employees?
• How do our wages stack up against those of other employers competing for our workers, and how does that compare with the study?
• Was an experiment conducted? If not, what approach was used to understand whether higher wages were driving the productivity change or simply reflecting it?
• What measures of productivity were used, and how long were the effects measured?
• What other analyses or data might be relevant?

Of course, tone matters. These questions must be asked in a genuine spirit of curiosity, with a desire to learn and get sound recommendations.

Whether evidence comes from an outside study or internal data, walking through it thoroughly before making major decisions is crucial. In our interactions with companies—including data-heavy tech firms—we’ve noticed that this practice isn’t consistently followed. Too often predetermined beliefs, problematic comparisons, and groupthink dominate discussions. Research from psychology and economics suggests that biases—such as base rate neglect, the tendency to overlook general statistical information in favor of specific case information or anecdotes, and confirmation bias, the propensity to seek out and overweight results that support your existing beliefs—also hinder the systematic weighing of evidence. But companies don’t have to fall into this pattern. Drawing on our research, work with companies, and teaching experience (including executive education classes in leadership and business analytics and a recent MBA course called Data-Driven Leadership), we have developed an approach general managers can apply to discussions of data so that they can make better decisions.